Biologist, PhD

Engagement: Hourly, at-will contractor (minimum 15 hrs/week). Fully remote and asynchronous.

About the Role

• Flexible workload — work from anywhere, on your own schedule
• High impact — your craft directly improves models used by top AI labs & Fortune 500 teams
• Clear ownership — know exactly what success looks like and have autonomy to deliver
• Growth potential — consistent high performers spearhead new programs and mentor incoming SMEs

Responsibilities

• Data annotation: Accurately label and categorize advanced biology questions, specialized concepts, scientific diagrams, experimental data, and other domain-specific content
• Concept mapping: Synthesize and connect complex biological principles, establishing nuanced relationships between specialized topics (e.g., molecular biology, genomics, systems biology, physiology) to help AI models grasp the depth and structure of the field
• Solution review: Critically analyze and verify AI-generated answers to advanced biology questions and research-level problems, identifying errors and providing detailed, constructive feedback to enhance model performance
• Content development: Design and curate comprehensive training datasets that encompass a wide spectrum of biology subfields and levels of complexity

Requirements

• You hold a PhD in Biology (or a closely related field) with deep expertise in research, teaching, or advanced problem-solving
• You have developed or critically reviewed complex scientific, educational, or technical content—such as peer-reviewed publications, advanced curricula, laboratory research, or sophisticated simulations
• You excel at communicating challenging physical concepts with clarity and enjoy translating advanced theory into real-world applications
• Bonus: Experience with data labeling, RLHF, or other AI training or model evaluation projects

Pay

Pay Range (US): $25 – $100 per hour

About Labelbox

Labelbox builds the data engine that accelerates breakthrough AI. Our platform, expert services, and marketplace let teams iterate on data as nimbly as they iterate on code, enabling safer, smarter models in production. We’re backed by SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures, Databricks Ventures, and Kleiner Perkins, and trusted by leading research labs and enterprises worldwide.